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Population-level estimates of sexual network mixing for parameterizing prediction models of pre-exposure prophylaxis (PrEP) effectiveness are needed to inform prevention of HIV transmission among men who have sex with men (MSM). Estimates obtained by egocentric sampling are vulnerable to information bias due to incomplete respondent knowledge.We estimated patterns of serosorting and PrEP sorting among MSM in the United States using data from a 2017-2019 egocentric sexual network study. Respondents served as proxies to report the HIV status and PrEP use of recent sexual partners. We contrasted results from a complete-case analysis (unknown HIV and PrEP excluded) versus a bias analysis with respondent-reported data stochastically reclassified to simulate unobserved self-reported data from sexual partners.We found strong evidence of preferential partnering across analytical approaches. The bias analysis showed concordance between sexual partners of HIV diagnosis and PrEP use statuses for MSM with diagnosed HIV (39%; 95% simulation interval: 31, 46), MSM who used PrEP (32%; 21, 37), and MSM who did not use PrEP (83%; 79, 87). The fraction of partners with diagnosed HIV was higher among MSM who used PrEP (11%; 9, 14) compared with MSM who did not use PrEP (4%; 3, 5). Comparatively, across all strata of respondents, the complete-case analysis overestimated the fractions of partners with diagnosed HIV or PrEP use.We found evidence consistent with HIV and PrEP sorting among MSM, which may decrease the population-level effectiveness of PrEP. Bias analyses can improve mixing estimates for parameterization of transmission models.

It is of interest to pinpoint SARS-CoV-2 sequence features defining vaccine resistance. In the ENSEMBLE randomized, placebo-controlled phase 3 trial, estimated single-dose Ad26.COV2.S vaccine efficacy (VE) was 56% against moderate to severe–critical COVID-19. SARS-CoV-2 Spike sequences were measured from 484 vaccine and 1,067 placebo recipients who acquired COVID-19 during the trial. In Latin America, where Spike diversity was greatest, VE was significantly lower against Lambda than against Reference and against all non-Lambda variants [family-wise error rate (FWER) p

Background The ALVAC/gp120 + MF59 vaccines in the HIV Vaccine Trials Network (HVTN) 702 efficacy trial did not prevent human immunodeficiency virus-1 (HIV-1) acquisition. Vaccine-matched immunological endpoints that were correlates of HIV-1 acquisition risk in RV144 were measured in HVTN 702 and evaluated as correlates of HIV-1 acquisition. Methods Among 1893 HVTN 702 female vaccinees, 60 HIV-1–seropositive cases and 60 matched seronegative noncases were sampled. HIV-specific CD4+ T-cell and binding antibody responses were measured 2 weeks after fourth and fifth immunizations. Cox proportional hazards models assessed prespecified responses as predictors of HIV-1 acquisition. Results The HVTN 702 Env-specific CD4+ T-cell response rate was significantly higher than in RV144 (63% vs 40%, P = .03) with significantly lower IgG binding antibody response rate and magnitude to 1086.C V1V2 (67% vs 100%, P Conclusions HVTN 702 and RV144 had distinct immunogenicity profiles. However, both identified significant correlations (univariate or interaction) for IgG V1V2 and polyfunctional CD4+ T cells with HIV-1 acquisition. Clinical Trials Registration . NCT02968849.

Antibody levels predict vaccine efficacy Symptomatic COVID-19 infection can be prevented by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines. A “correlate of protection” is a molecular biomarker to measure how much immunity is needed to fight infection and is key for successful global immunization programs. Gilbert et al . determined that antibodies are the correlate of protection in vaccinated individuals enrolled in the Moderna COVE phase 3 clinical trial (see the Perspective by Openshaw). By measuring binding and neutralizing antibodies against the viral spike protein, the authors found that the levels of both antibodies correlated with the degree of vaccine efficacy. The higher the antibody level, the greater the protection afforded by the messenger RNA (mRNA) vaccine. Antibody levels that predict mRNA vaccine efficacy can therefore be used to guide vaccine regimen modifications and support regulatory approvals for a broader spectrum of the population. —PNK

Recent studies have indicated that it is possible to protect individuals from HIV infection using passive infusion of monoclonal antibodies. However, in order for monoclonal antibodies to confer robust protection, the antibodies must be capable of neutralizing many possible strains of the virus. This is particularly challenging in the context of a highly diverse pathogen like HIV. It is therefore of great interest to leverage existing observational data sources to discover antibodies that are able to neutralize HIV viruses via residues where existing antibodies show modest protection. Such information feeds directly into the clinical trial pipeline for monoclonal antibody therapies by providing information on (i) whether and to what extent combinations of antibodies can generate superior protection and (ii) strategies for analyzing past clinical trials to identify in vivo evidence of antibody resistance. These observational data include genetic features of many diverse HIV genetic sequences, as well as in vitro measures of antibody resistance. The statistical learning problem we are interested in is developing statistical methodology that can be used to analyze these data to identify important genetic features that are significantly associated with antibody resistance. This is a challenging problem owing to the high-dimensional and strongly correlated nature of the genetic sequence data. To overcome these challenges, we propose an outcome-adaptive, collaborative targeted minimum loss-based estimation approach using random forests. We demonstrate via simulation that the approach enjoys important statistical benefits over existing approaches in terms of bias, mean squared error, and type I error. We apply the approach to the Compile, Analyze, and Tally Nab Panels database to identify AA positions that are potentially causally related to resistance to neutralization by several different antibodies.

In the PREVENT-19 phase 3 trial of the NVX-CoV2373 vaccine (NCT04611802), anti-spike binding IgG concentration (spike IgG), anti-RBD binding IgG concentration (RBD IgG), and pseudovirus 50% neutralizing antibody titer (nAb ID50) measured two weeks post-dose two are assessed as correlates of risk and as correlates of protection against COVID-19. Analyses are conducted in the U.S. cohort of baseline SARS-CoV-2 negative per-protocol participants using a case-cohort design that measures the markers from all 12 vaccine recipient breakthrough COVID-19 cases starting 7 days post antibody measurement and from 639 vaccine recipient non-cases. All markers are inversely associated with COVID-19 risk and directly associated with vaccine efficacy. In vaccine recipients with nAb ID50 titers of 50, 100, and 7230 international units (IU50)/ml, vaccine efficacy estimates are 75.7% (49.8%, 93.2%), 81.7% (66.3%, 93.2%), and 96.8% (88.3%, 99.3%). The results support potential cross-vaccine platform applications of these markers for guiding decisions about vaccine approval and use.

BackgroundThe Eastern European country of Georgia initiated a nationwide hepatitis C virus (HCV) elimination program in 2015 to address a high burden of infection. Screening for HCV infection through antibody testing was integrated into multiple existing programs, including the National Tuberculosis Program (NTP). We sought to evaluate loss to follow-up (LTFU) from the hepatitis C care cascade among persons diagnosed with active tuberculosis (TB) disease.MethodsUsing national ID numbers, we merged databases of the HCV elimination program, NTP, and national death registry from January 1, 2015, to September 30, 2020. We estimated the proportion of patients with and without TB who were LTFU at each step of the HCV care cascade and explored temporal changes.ResultsAmong 11,985 patients with active TB, 9,065 (76%) were tested for HCV antibodies, and 1,665 (18%) had a positive result; LTFU from hepatitis C care was common, with 20% of patients with a positive antibody test not undergoing viremia testing, and 43% of patients with viremia not starting treatment for hepatitis C. Overall, among persons with confirmed viremic HCV infection, only 28% of patients with TB had a documented cure from HCV infection, compared to 55% among patients without TB. LTFU after positive antibody testing substantially decreased in the last three years, from 32% among patients diagnosed with TB in 2017 to 12% among those diagnosed in 2019.ConclusionsLTFU from hepatitis C care after a positive antibody or viremia test was high and more common among patients with TB than in those without TB. Better integration of TB and hepatitis C care systems can potentially reduce LTFU and improve patient outcomes. Existing large-scale programs for both TB and hepatitis C in Georgia create a unique opportunity for such integration to contribute to hepatitis C elimination efforts.

Background Hepatitis C virus (HCV) infection causes dysregulation and suppression of immune pathways involved in the control of tuberculosis (TB) infection. However, data on the role of chronic hepatitis C as a risk factor for active TB are lacking. We sought to evaluate the association between HCV infection and the development of active TB. Methods We conducted a cohort study in Georgia among adults tested for HCV antibodies (January 2015–September 2020) and followed longitudinally for the development of newly diagnosed active TB. Data were obtained from the Georgian national programs of hepatitis C and TB. The exposures of interest were untreated and treated HCV infection. A Cox proportional hazards model was used to calculate adjusted hazard ratios (aHRs). Results A total of 1 828 808 adults were included (median follow-up time: 26 months; IQR: 13–39 months). Active TB was diagnosed in 3163 (0.17%) individuals after a median of 6 months follow-up (IQR: 1–18 months). The incidence rate per 100 000 person-years was 296 among persons with untreated HCV infection, 109 among those with treated HCV infection, and 65 among HCV-negative persons. In multivariable analysis, both untreated (aHR = 2.9; 95% CI: 2.4–3.4) and treated (aHR = 1.6; 95% CI: 1.4–2.0) HCV infections were associated with a higher hazard of active TB, compared with HCV-negative persons. Conclusions Adults with HCV infection, particularly untreated individuals, were at higher risk of developing active TB disease. Screening for latent TB infection and active TB disease should be part of clinical evaluation of people with HCV infection, especially in high-TB-burden areas.

We consider estimation of a functional parameter of a realistically modeled data distribution based on observing independent and identically distributed observations. We define an $m$-th order Spline Highly Adaptive Lasso Minimum Loss Estimator (Spline HAL-MLE) of a functional parameter that is defined by minimizing the empirical risk function over an $m$-th order smoothness class of functions. We show that this $m$-th order smoothness class consists of all functions that can be represented as an infinitesimal linear combination of tensor products of $\leq m$-th order spline-basis functions, and involves assuming $m$-derivatives in each coordinate. By selecting $m$ with cross-validation we obtain a Spline-HAL-MLE that is able to adapt to the underlying unknown smoothness of the true function, while guaranteeing a rate of convergence faster than $n^{-1/4}$, as long as the true function is cadlag (right-continuous with left-hand limits) and has finite sectional variation norm. The $m=0$-smoothness class consists of all cadlag functions with finite sectional variation norm and corresponds with the original HAL-MLE defined in van der Laan (2015). In this article we establish that this Spline-HAL-MLE yields an asymptotically efficient estimator of any smooth feature of the functional parameter under an easily verifiable global undersmoothing condition. A sufficient condition for the latter condition is that the minimum of the empirical mean of the selected basis functions is smaller than a constant times $n^{-1/2}$, which is not parameter specific and enforces the selection of the $L_1$-norm in the lasso to be large enough to include sparsely supported basis. We demonstrate our general result for the $m=0$-HAL-MLE of the average treatment effect and of the integral of the square of the data density. We also present simulations for these two examples confirming the theory.

Motivation A single monoclonal broadly neutralizing antibody (bnAb) regimen was recently evaluated in two randomized trials for prevention efficacy against HIV-1 infection. Subsequent trials will evaluate combination bnAb regimens (e.g. cocktails, multi-specific antibodies), which demonstrate higher potency and breadth in vitro compared to single bnAbs. Given the large number of potential regimens, methods for down-selecting these regimens into efficacy trials are of great interest. Results We developed Super LeArner Prediction of NAb Panels (SLAPNAP), a software tool for training and evaluating machine learning models that predict in vitro neutralization sensitivity of HIV Envelope (Env) pseudoviruses to a given single or combination bnAb regimen, based on Env amino acid sequence features. SLAPNAP also provides measures of variable importance of sequence features. By predicting bnAb coverage of circulating sequences, SLAPNAP can improve ranking of bnAb regimens by their potential prevention efficacy. In addition, SLAPNAP can improve sieve analysis by defining sequence features that impact bnAb prevention efficacy. Availabilityand implementation SLAPNAP is a freely available docker image that can be downloaded from DockerHub (https://hub.docker.com/r/slapnap/slapnap). Source code and documentation are available at GitHub (https://github.com/benkeser/slapnap and https://benkeser.github.io/slapnap/).

Safe and effective vaccines are a critical component in the control of COVID-19. A group of industry, government, and academic researchers discuss pragmatic issues in the choice and interpretation of clinical endpoints for evaluating efficacy in COVID-19 vaccine trials., Several vaccine candidates to protect against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or coronavirus disease 2019 (COVID-19) have entered or will soon enter large-scale, phase 3, placebo-controlled randomized clinical trials. To facilitate harmonized evaluation and comparison of the efficacy of these vaccines, a general set of clinical endpoints is proposed, along with considerations to guide the selection of the primary endpoints on the basis of clinical and statistical reasoning. The plausibility that vaccine protection against symptomatic COVID-19 could be accompanied by a shift toward more SARS-CoV-2 infections that are asymptomatic is highlighted, as well as the potential implications of such a shift.

Background Patients with firearm injuries are at high risk of subsequent arrest and injury following hospital discharge. We sought to evaluate the effect of a 6-month joint hospital- and community-based low-intensity intervention on risk of arrest and injury among patients with firearm injuries. Methods We conducted a cluster randomized controlled trial, enrolling patients with firearm injuries who received treatment at Harborview Medical Center, the Level 1 trauma center in Seattle, Washington, were 18 years or older at the time of injury, spoke English, were able to provide consent and a method of contact, and lived in one of the five study counties. The intervention consisted of hospital-based motivational interviewing, followed by a 6-month community-based intervention, and multiagency support. The primary outcome was the risk of subsequent arrest. The main secondary outcome was the risk of death or subsequent injury requiring treatment in the emergency department or hospitalization. Results Neither assignment to, or engagement with, the intervention, defined as having at least 1 contact point with the Support Specialist, was associated with risk of arrest at 2 years post hospital discharge (Relative Risk for intervention assignment = 1.15; 95% CI: 0.90, 1.48; Relative Risk for intervention engagement = 1.07; 95% CI: 0.74, 2.19). There was similarly no association observed for subsequent injury. Conclusions This study represents one of the first randomized controlled trials of a joint hospital- and community-based intervention delivered exclusively among patients with firearm injuries. The intervention was not associated with changes in risk of arrest or injury, a finding most likely due to the low intensity of the program. Level of evidence Care Management, level II.RegistrationRegistered at clinicaltrials.gov NCT02630225.

Understanding the pathways whereby an intervention has an effect on an outcome is a common scientific goal. A rich body of literature provides various decompositions of the total intervention effect into pathway-specific effects. Interventional direct and indirect effects provide one such decomposition. Existing estimators of these effects are based on parametric models with confidence interval estimation facilitated via the nonparametric bootstrap. We provide theory that allows for more flexible, possibly machine learning-based, estimation techniques to be considered. In particular, we establish weak convergence results that facilitate the construction of closed-form confidence intervals and hypothesis tests and prove multiple robustness properties of the proposed estimators. Simulations show that inference based on large-sample theory has adequate small-sample performance. Our work thus provides a means of leveraging modern statistical learning techniques in estimation of interventional mediation effects.

Measuring immune correlates of disease acquisition and protection in the context of a clinical trial is a prerequisite for improved vaccine design. We analysed binding and neutralizing antibody measurements 4 weeks post vaccination as correlates of risk of moderate to severe-critical COVID-19 through 83 d post vaccination in the phase 3, double-blind placebo-controlled phase of ENSEMBLE, an international randomized efficacy trial of a single dose of Ad26.COV2.S. We also evaluated correlates of protection in the trial cohort. Of the three antibody immune markers we measured, we found most support for 50% inhibitory dilution (ID

In the randomized, placebo-controlled PREVENT-19 phase 3 trial conducted in the U.S. and Mexico of the NVX-CoV2373 adjuvanted, recombinant spike protein nanoparticle vaccine, anti-spike binding IgG concentration (spike IgG) and pseudovirus 50% neutralizing antibody titer (nAb ID50) measured two weeks after two doses were assessed as correlates of risk and as correlates of protection against PCR-confirmed symptomatic SARS-CoV-2 infection (COVID- 19). These immune correlates analyses were conducted in the U.S. cohort of baseline SARS- CoV-2 negative per-protocol participants using a case-cohort design that measured the antibody markers from all 12 vaccine recipient breakthrough COVID-19 cases starting 7 days post antibody measurement and from 639 vaccine recipient non-cases (Mexico was excluded due to zero breakthrough cases with the efficacy data cut-off date April 19, 2021). In vaccine recipients, the baseline risk factor-adjusted hazard ratio of COVID-19 was 0.36 (95% CI: 0.20, 0.63), prdpercentile, 6934 is the 97.5thpercentile), vaccine efficacy to reduce the probability of acquiring COVID-19 at 59 days post marker measurement was 65.5% (95% CI: 23.0%, 90.8%), 87.7% (77.7%, 94.4%), and 94.8% (88.0%, 97.9%), respectively. At nAb ID50 titers of 50, 100, 1000, and 7230 IU50/ml (50 is the 5thpercentile, 7230 the 97.5thpercentile), these estimates were 75.7% (49.8%, 93.2%), 81.7% (66.3%, 93.2%), 92.8% (85.1%, 97.4%) and 96.8% (88.3%, 99.3%). The same two antibody markers were assessed as immune correlates via the same study design and statistical analysis in the mRNA-1273 phase 3 COVE trial (except in COVE the markers were measured four weeks post dose two). Spike IgG levels were slightly lower and nAb ID50 titers slightly higher after NVX-CoV2373 than after mRNA-1273 vaccination. The strength of the nAb ID50 correlate was similar between the trials, whereas the spike IgG antibodies appeared to correlate more strongly with NVX-CoV2373 in PREVENT-19, as quantified by the hazard ratio and the degree of change in vaccine efficacy across antibody levels. However, the relatively few breakthrough cases in PREVENT-19 limited the ability to infer a stronger correlate. The conclusion is that both markers were consistent correlates of protection for the two vaccines, supporting potential cross-vaccine platform applications of these markers for guiding decisions about vaccine approval and use.

Anti-spike IgG binding antibody, anti-receptor binding domain IgG antibody, and pseudovirus neutralizing antibody measurements four weeks post-vaccination were assessed as correlates of risk of moderate to severe-critical COVID-19 outcomes through 83 days post-vaccination and as correlates of protection following a single dose of Ad26.COV2.S COVID-19 vaccine in the placebo-controlled phase of ENSEMBLE, an international, randomized efficacy trial. Each marker had evidence as a correlate of risk and of protection, with strongest evidence for 50% inhibitory dilution (ID50) neutralizing antibody titer. The outcome hazard ratio was 0.49 (95% confidence interval 0.29, 0.81; p=0.006) per 10-fold increase in ID50; vaccine efficacy was 60% (43, 72%) at nonquantifiable ID50 (< 2.7 IU50/ml) and rose to 89% (78, 96%) at ID50 = 96.3 IU50/ml. Comparison of the vaccine efficacy by ID50 titer curves for ENSEMBLE-US, the COVE trial of the mRNA-1273 vaccine, and the COV002-UK trial of the AZD1222 vaccine supported consistency of the ID50 titer correlate of protection across trials and vaccine types.

BACKGROUND: Recent evidence suggests transmission of Mycobacterium tuberculosis (Mtb) may be characterized by extreme individual heterogeneity in secondary cases (i.e., few cases account for the majority of transmission). Such heterogeneity implies outbreaks are rarer but more extensive and has profound implications in infectious disease control. However, discrete person-to-person transmission events in tuberculosis (TB) are often unobserved, precluding our ability to directly quantify individual heterogeneity in TB epidemiology. METHODS: We used a modified negative binomial branching process model to quantify the extent of individual heterogeneity using only observed transmission cluster size distribution data (i.e., the simple sum of all cases in a transmission chain) without knowledge of individual-level transmission events. The negative binomial parameter k quantifies the extent of individual heterogeneity (generally, k < 1 indicates extensive heterogeneity, and as k → ∞ transmission becomes more homogenous). We validated the robustness of the inference procedure considering common limitations affecting cluster size data. Finally, we demonstrate the epidemiologic utility of this method by applying it to aggregate US molecular surveillance data from the US Centers for Disease Control and Prevention. RESULTS: The cluster-based method reliably inferred k using TB transmission cluster data despite a high degree of bias introduced into the model. We found that the TB transmission in the United States was characterized by a high propensity for extensive outbreaks ([Formula: see text]; 95% confidence interval = 0.09, 0.10). CONCLUSIONS: The proposed method can accurately quantify critical parameters that govern TB transmission using simple, more easily obtainable cluster data to improve our understanding of TB epidemiology.

Background Vaccines may play a role in controlling the spread of antibiotic resistance. However, it is unknown if rotavirus vaccination affects antibiotic use in the United States (US). Methods Using data from the IBM MarketScan Commercial Database, we conducted a retrospective cohort of US children born between 2007 and 2018 who were continuously enrolled for the first 8 months of life (N = 2 136 136). We followed children through 5 years of age and compared children who completed a full rotavirus vaccination series by 8 months of age to children who had not received any doses of rotavirus vaccination. We evaluated antibiotic prescriptions associated with an acute gastroenteritis (AGE) diagnosis and defined the switching of antibiotics as the prescription of a second, different antibiotic within 28 days. Using a stratified Kaplan-Meier approach, we estimated the cumulative incidence for each study group, adjusted for receipt of pneumococcal conjugate vaccine, provider type, and urban/rural status. Results Overall, 0.8% (n = 17 318) of participants received an antibiotic prescription following an AGE diagnosis. The 5-year adjusted relative cumulative incidence of antibiotic prescription following an AGE diagnosis was 0.793 (95% confidence interval [CI], .761–.827) among children with complete rotavirus vaccination compared to children without rotavirus vaccination. Additionally, children with complete vaccination were less likely to switch antibiotics (0.808 [95% CI, .743–.887]). Rotavirus vaccination has averted an estimated 67 045 (95% CI, 53 729–80 664) antibiotic prescriptions nationally among children born between 2007 and 2018. Conclusions These results demonstrate that rotavirus vaccines reduce antibiotic prescribing for AGE, which could help reduce the growth of antibiotic resistance.

The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder in children without an intellectual disability to illustrate the problem and the potential solution. We aggregated data from 545 children (8-13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 28.5% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 81.0% and 60.1% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.

The protective effects of vaccines may vary depending on individual characteristics, such as age. Traditionally, such effect modification has been examined with subgroup analyses or inclusion of cross-product terms in regression frameworks. However, in many vaccine settings, effect modification may also depend on the infecting pathogen's characteristics, which are measured postrandomization. Sieve analysis examines whether such effects are present by combining pathogen genetic sequence information with individual-level data and can generate new hypotheses on the pathways whereby vaccines provide protection. In this article, we develop a causal framework for evaluating effect modification in the context of sieve analysis. Our approach can be used to assess the magnitude of sieve effects and, in particular, whether these effects are modified by individual-level characteristics. Our method accounts for difficulties occurring in real-world data analysis, such as competing risks, nonrandomized treatments, and differential dropout. Our approach also integrates modern machine learning techniques. We demonstrate the validity and efficiency of our approach in simulation studies and apply the methodology to a malaria vaccine study.

There are important challenges to the estimation and identification of average causal effects in longitudinal data with time-varying exposures. Here, we discuss the difficulty in meeting the positivity condition. Our motivating example is the per-protocol analysis of the Effects of Aspirin in Gestation and Reproduction (EAGeR) Trial. We estimated the average causal effect comparing the incidence of pregnancy by 26 weeks that would have occurred if all women had been assigned to aspirin and complied versus the incidence if all women had been assigned to placebo and complied. Using flexible targeted minimum loss-based estimation, we estimated a risk difference of 1.27% (95% CI: −9.83, 12.38). Using a less flexible inverse probability weighting approach, the risk difference was 5.77% (95% CI: −1.13, 13.05). However, the cumulative probability of compliance conditional on covariates approached 0 as follow-up accrued, indicating a practical violation of the positivity assumption, which limited our ability to make causal interpretations. The effects of nonpositivity were more apparent when using a more flexible estimator, as indicated by the greater imprecision. When faced with nonpositivity, one can use a flexible approach and be transparent about the uncertainty, use a parametric approach and smooth over gaps in the data, or target a different estimand that will be less vulnerable to positivity violations.

Background The safety and efficacy of the AZD1222 (ChAdOx1 nCoV-19) vaccine in a large, diverse population at increased risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the United States, Chile, and Peru has not been known. Methods In this ongoing, double-blind, randomized, placebo-controlled, phase 3 clinical trial, we investigated the safety, vaccine efficacy, and immunogenicity of two doses of AZD1222 as compared with placebo in preventing the onset of symptomatic and severe coronavirus disease 2019 (Covid-19) 15 days or more after the second dose in adults, including older adults, in the United States, Chile, and Peru. Results A total of 32,451 participants underwent randomization, in a 2:1 ratio, to receive AZD1222 (21,635 participants) or placebo (10,816 participants). AZD1222 was safe, with low incidences of serious and medically attended adverse events and adverse events of special interest; the incidences were similar to those observed in the placebo group. Solicited local and systemic reactions were generally mild or moderate in both groups. Overall estimated vaccine efficacy was 74.0% (95% confidence interval [CI], 65.3 to 80.5; P

BackgroundIn the Coronavirus Efficacy (COVE) trial, estimated mRNA-1273 vaccine efficacy against coronavirus disease-19 (COVID-19) was 94%. SARS-CoV-2 antibody measurements were assessed as correlates of COVID-19 risk and as correlates of protection.MethodsThrough case-cohort sampling, participants were selected for measurement of four serum antibody markers at Day 1 (first dose), Day 29 (second dose), and Day 57: IgG binding antibodies (bAbs) to Spike, bAbs to Spike receptor-binding domain (RBD), and 50% and 80% inhibitory dilution pseudovirus neutralizing antibody titers calibrated to the WHO International Standard (cID50 and cID80). Participants with no evidence of previous SARS-CoV-2 infection were included. Cox regression assessed in vaccine recipients the association of each Day 29 or 57 serologic marker with COVID-19 through 126 or 100 days of follow-up, respectively, adjusting for risk factors.ResultsDay 57 Spike IgG, RBD IgG, cID50, and cID80 neutralization levels were each inversely correlated with risk of COVID-19: hazard ratios 0.66 (95% CI 0.50, 0.88; p=0.005); 0.57 (0.40, 0.82; p=0.002); 0.42 (0.27, 0.65; pConclusionsBinding and neutralizing antibodies correlated with COVID-19 risk and vaccine efficacy and likely have utility in predicting mRNA-1273 vaccine efficacy against COVID-19.Trial registration numberCOVEClinicalTrials.govnumber,NCT04470427

Background: Several candidate vaccines to prevent COVID-19 disease have entered large-scale phase 3 placebo-controlled randomized clinical trials and some have demonstrated substantial short-term efficacy. Efficacious vaccines should, at some point, be offered to placebo participants, which will occur before long-term efficacy and safety are known. Methods: Following vaccination of the placebo group, we show that placebo-controlled vaccine efficacy can be derived by assuming the benefit of vaccination over time has the same profile for the original vaccine recipients and the placebo crossovers. This reconstruction allows estimation of both vaccine durability and potential vaccine-associated enhanced disease. Results: Post-crossover estimates of vaccine efficacy can provide insights about durability, identify waning efficacy, and identify late enhancement of disease, but are less reliable estimates than those obtained by a standard trial where the placebo cohort is maintained. As vaccine efficacy estimates for post-crossover periods depend on prior vaccine efficacy estimates, longer pre-crossover periods with higher case counts provide better estimates of late vaccine efficacy. Further, open-label crossover may lead to riskier behavior in the immediate crossover period for the unblinded vaccine arm, confounding vaccine efficacy estimates for all post-crossover periods. Conclusions: We advocate blinded crossover and continued follow-up of trial participants to best assess vaccine durability and potential delayed enhancement of disease. This approach allows placebo recipients timely access to the vaccine when it would no longer be proper to maintain participants on placebo, yet still allows important insights about immunological and clinical effectiveness over time.

Background A safe, effective vaccine is essential to eradicating human immunodeficiency virus (HIV) infection. A canarypox-protein HIV vaccine regimen (ALVAC-HIV plus AIDSVAX B/E) showed modest efficacy in reducing infection in Thailand. An analogous regimen using HIV-1 subtype C virus showed potent humoral and cellular responses in a phase 1-2a trial in South Africa. Efficacy data and additional safety data were needed for this regimen in a larger population in South Africa. Methods In this phase 2b-3 trial, we randomly assigned 5404 adults without HIV-1 infection to receive the vaccine (2704 participants) or placebo (2700 participants). The vaccine regimen consisted of injections of ALVAC-HIV at months 0 and 1, followed by four booster injections of ALVAC-HIV plus bivalent subtype C gp120-MF59 adjuvant at months 3, 6, 12, and 18. The primary efficacy outcome was the occurrence of HIV-1 infection from randomization to 24 months. Results In January 2020, prespecified criteria for nonefficacy were met at an interim analysis; further vaccinations were subsequently halted. The median age of the trial participants was 24 years; 70% of the participants were women. The incidence of adverse events was similar in the vaccine and placebo groups. During the 24-month follow-up, HIV-1 infection was diagnosed in 138 participants in the vaccine group and in 133 in the placebo group (hazard ratio, 1.02; 95% confidence interval, 0.81 to 1.30; P = 0.84). Conclusions The ALVAC-gp120 regimen did not prevent HIV-1 infection among participants in South Africa despite previous evidence of immunogenicity. (HVTN 702 ClinicalTrials.gov number, NCT02968849.).

Continued follow-up of participants in COVID-19 vaccine trials after vaccination of the placebo group permits determination of vaccine durability and allows the incorporation of a booster trial if waning vaccine efficacy is observed, while ensuring the ethical treatment of participants., Multiple candidate vaccines to prevent COVID-19 have entered large-scale phase 3 placebo-controlled randomized clinical trials, and several have demonstrated substantial short-term efficacy. At some point after demonstration of substantial efficacy, placebo recipients should be offered the efficacious vaccine from their trial, which will occur before longer-term efficacy and safety are known. The absence of a placebo group could compromise assessment of longer-term vaccine effects. However, by continuing follow-up after vaccination of the placebo group, this study shows that placebo-controlled vaccine efficacy can be mathematically derived by assuming that the benefit of vaccination over time has the same profile for the original vaccine recipients and the original placebo recipients after their vaccination. Although this derivation provides less precise estimates than would be obtained by a standard trial where the placebo group remains unvaccinated, this proposed approach allows estimation of longer-term effect, including durability of vaccine efficacy and whether the vaccine eventually becomes harmful for some. Deferred vaccination, if done open-label, may lead to riskier behavior in the unblinded original vaccine group, confounding estimates of long-term vaccine efficacy. Hence, deferred vaccination via blinded crossover, where the vaccine group receives placebo and vice versa, would be the preferred way to assess vaccine durability and potential delayed harm. Deferred vaccination allows placebo recipients timely access to the vaccine when it would no longer be proper to maintain them on placebo, yet still allows important insights about immunologic and clinical effectiveness over time.

We aim to help inform the choice of estimand (i.e., target of inference) and analysis method to be used in future COVID-19 treatment trials. To this end, we describe estimands for outcome types of particular interest in these trials (ordinal and time-to-event). When the outcome is ordinal, the estimands that we consider are the difference between study arms in the mean outcome, the Mann-Whitney (rank--based) estimand, and the average of the cumulative log odds ratios over the levels of the outcome. For time-to-event outcomes, we consider the difference between arms in the restricted mean survival time, the difference between arms in the cumulative incidence, and the relative risk. Advantageously, the interpretability of these estimands does not rely on a proportional odds or proportional hazards assumptions. For each estimand, we evaluate the potential value added by using estimators that leverage information in baseline variables to improve precision and reduce the required sample size to achieve a desired power. These are called covariate adjusted estimators. To evaluate the performance of the covariate adjusted and unadjusted estimators that we present, we simulate two-arm, randomized trials comparing a hypothetical COVID-19 treatment versus standard of care, where the primary outcome is ordinal or time-to-event. Our simulated distributions are derived from two sources: longitudinal data on over 500 patients hospitalized at Weill Cornell Medicine New York Presbyterian Hospital prior to March 28, 2020, and a CDC preliminary description of 2449 cases reported to the CDC from February 12 to March 16, 2020. We focus on hospitalized, COVID-19 positive patients and consider the following outcomes: intubation, ventilator use, and death. We conduct simulations using all three estimands when the outcome is ordinal, but only evaluate the restricted mean survival time when the outcome is time to event. Our simulations showed that, in trials with at least 200 participants, the precision gains due to covariate adjustment are equivalent to requiring 10-20% fewer participants to achieve the same power as a trial that uses the unadjusted estimator; this was the case for each outcome and estimand that we considered.